MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541123145 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy Vishwavidyapeeth; Malla Reddy University; Malla Reddy Engineering College For Women; and Malla Reddy College Of Engineering And Technology, Medchal-Malkajgiri, Telangana, on Dec. 6, 2025, for 'autonomous data cleaning agent for noisy sensor environments.'
Inventor(s) include Dr. Tirumala Paruchuri; Mr. Thummapudi Venkata Seshu Kiran; Dr. M. Chalapathi Rao; Mr. Ayub Baig; and Kkoushil Reddy.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "The current invention reveals a state-of-the-art Autonomous Data Cleaning Agent which is especially developed to solve the signal degradation phenomenon of noisy sensor environment. In industrial IoT (IIoT) and remote monitoring, sensors are often exposed to strenuous environments that add the noise of different types, including high frequency electromagnetic interferences with sensors, sensor drift and baseline wandering. Traditional methods of filtering mostly use fixed parameters that cannot manage the dynamic nature of these disturbances resulting in either under filtering where noise is retained or over filtering where useful signal data may be lost. The invention removes these constraints by putting an intelligent agent on the job, which is able to diagnose the particular type of noise profile in real-time and actively choosing the most suitable remediation strategy. The system is designed as a multi-layered system that lies between the downstream analytics platform and the layer of the raw sensor data acquisition. This agent does not use predefined filters like that used in traditional hard-coded filters but uses a lightweight inference engine to examine the statistical properties of the incoming data stream, e.g., variance, kurtosis, and spectral density. On the basis of such an analysis, the agent separates the real signal anomalies, which are real world physical phenomena, and those that arise due to environmental noise or system failure. This difference is very important in applications like predictive maintenance which may be used to respond to a sudden spike, which could indicate machinery failure instead of merely being a sensor spike. Lastly, agent autonomy minimizes the amount of operational overhead required to operate large scale sensor networks. With a traditional configuration, data engineers have to manually tune the filters on various sensors, which is incompatible with networks of thousands of nodes. This calibration is automated by the current invention making it easy to deploy and scale. The outcome is an efficient, self-sustaining data pipeline that guarantees the high-fidelity inputs to critical decision-making procedures in such industries as manufacturing and agriculture as well as smart city infrastructure."
Disclaimer: Curated by HT Syndication.
